A Data-Driven Method Embedded with Topological Information for Voltage-Power Sensitivity Estimation in Distribution Network

doi:10.16183/j.cnki.jsjtu.2022.485

Abstract

Abstract:

The multicollinearity of measurement data leads to the low accuracy of the data-driven methods for estimating voltage-power sensitivity in distribution networks. In this paper, a data-driven method embedded with topological information is proposed to address the problem. First, the voltage-power sensitivity matrix is decomposed into principal and secondary components, where the principal component is closely related to the distribution network topology and the secondary component is the error between the principal component and the actual value. Then, the principal and secondary components are estimated sequentially in two stages, and their data-driven estimation models based on quadratic programming are established, respectively. The key of the model in the first stage is the constraint based on the distribution network topology information, and the key of the model in the second stage is the constraint that the ratio of the secondary component to the principal component is tiny. Finally, the accuracy and efficiency of the proposed method is validated in the IEEE 33-bus system with a set of measurement data, and comparisons are made with ordinary least square regression, ridge regression, and LASSO regression. The simulation results show that the accuracy of the proposed method is significantly improved by orders of magnitude.

Key words: voltage-power sensitivity, distribution network topology, multicollinearity, data-driven, principal component

CLC Number:

TM732

LIU Shu, ZHOU Min, GAO Yuanhai, XU Xiaoyuan, YAN Zheng. A Data-Driven Method Embedded with Topological Information for Voltage-Power Sensitivity Estimation in Distribution Network[J]. Journal of Shanghai Jiao Tong University, 2024, 58(6): 855-862.

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估计方法	x_NMSE	最大误差 (p.u.)	误差中位数(p.u.)
常规最小二乘回归	5.2×10^-1	1.1×10^-1	3.5×10^-3
岭回归	9.3×10^-2	3.5×10^-2	9.6×10^-4
LASSO回归	7.1×10^-2	2.8×10^-2	2.3×10^-3
本文	2.5×10^-3	9.2×10^-3	1.7×10^-4

参数组合	x_NMSE	参数组合	x_NMSE
λ=4, ξ=0	2.7×10^-3	λ=1, ξ=0.1	2.0×10^-3
λ=4, ξ=0.1	2.5×10^-3	λ=2, ξ=0.1	2.4×10^-3
λ=4, ξ=0.2	2.5×10^-3	λ=4, ξ=0.1	2.5×10^-3
λ=4, ξ=0.4	2.5×10^-3	λ=8, ξ=0.1	2.8×10^-3
λ=4, ξ=0.8	3.1×10^-3	λ=10, ξ=0.1	2.8×10^-3
λ=4, ξ=1.0	3.4×10^-3	λ=100, ξ=0.1	2.8×10^-3

岭回归	x_NMSE	LASSO回归	x_NMSE
β=2×10^-11	20.3×10^-2	γ=20	28.7×10^-2
β=4×10^-11	17.4×10^-2	γ=40	7.7×10^-2
β=8×10^-11	9.3×10^-2	γ=60	7.1×10^-2
β=16×10^-11	9.5×10^-2	γ=80	9.3×10^-2
β=32×10^-11	9.8×10^-2	γ=100	13.7×10^-2
β=64×10^-11	10.3×10^-2	γ=200	28.9×10^-2

估计方法	耗时/s
常规最小二乘回归	0.3
岭回归	0.3
LASSO回归	0.45
本文	0.54